Release Highlights for IMP.bff 0.12.0

We are pleased to announce the release of IMP.bff 0.12, which comes with many bug fixes and new features! We detail below a few of the major features of this release. For an exhaustive list of all the changes, please refer to the release notes.

To install the latest version with conda:

conda install -c tpeulen imp.bff

import numpy as np
import pylab as plt
import pathlib

import IMP
import IMP.core
import IMP.atom
import IMP.bff
import IMP.em

Path searches

The bff.PathMap now supports A* and Djikstra path searches to find. This means that optimal path between points (e.g., for the surface accessible distances) for between two points (A*) or multiple points (Djikstra) can be searched.

m = IMP.Model()
pdb_fn = pathlib.Path(IMP.bff.get_example_path('structure')) / "T4L/3GUN.pdb"
hier = IMP.atom.read_pdb(str(pdb_fn), m)

sel = IMP.atom.Selection(hier)
sel.set_chain_id('A')
sel.set_residue_index(132)
sel.set_atom_type(IMP.atom.AtomType('CB'))
sel_particles = sel.get_selected_particles()
labeled_p = IMP.core.XYZ(sel_particles[0])
path_origin = labeled_p.get_coordinates()
path_map_header_parameter = {
    "max_path_length": 20.0,
    "grid_spacing": 1.0,
    "neighbor_radius": 2,
    "obstacle_threshold": 1e-5
}
path_map_header = IMP.bff.PathMapHeader(**path_map_header_parameter)
path_map_header.set_path_origin(path_origin)
path_map = IMP.bff.PathMap(path_map_header)

ps = [a.get_particle() for a in IMP.atom.get_leaves(hier)]
path_map.set_particles(ps)
path_map.sample_obstacles(extra_radius=0.0)
# The origin is located on an atom and hence blocked. Thus, there cannot be
# a path to the origin. Hence, unblock sphere around the origin.
path_map.fill_sphere(path_origin, radius=3.0, value=0.0, inverse=False)
path_map.update_tiles()
start_idx = path_map.get_voxel_by_location(path_origin)
path_map.find_path_dijkstra(start_idx, -1)  # if the end_idx

t = path_map.get_tiles()
end_idx_1 = 829
path_1 = t[end_idx_1].backtrack_to_path()
print(path_1)

end_idx_2 = 229
path_2 = t[end_idx_2].backtrack_to_path()
print(path_2)

(32820, 31180, 29540, 27900, 24700, 21500, 18300, 16701, 15102, 13503, 10303, 8704, 7105, 5506, 3947, 2388, 829)
(32820, 31180, 29540, 27901, 26301, 24662, 23023, 21383, 19743, 18103, 16463, 13263, 11624, 9985, 8346, 6707, 5068, 3429, 229)

The most important attributes of tiles are the penalty and the cost of visiting a tile (from the starting point). Additionally, a tile has a density, e.g., used to implement weighted accessible volumes and a set of other (user-defined) features that are stored in a dictionary.

bounds = 0.0, 30
BFF_TILE_PENALTY = path_map.get_tile_values(IMP.bff.PM_TILE_PENALTY, (0, 1))
PM_TILE_COST = path_map.get_tile_values(IMP.bff.PM_TILE_COST, bounds)
PM_TILE_COST_DENSITY = path_map.get_tile_values(IMP.bff.PM_TILE_COST_DENSITY, bounds)

plt.imshow(PM_TILE_COST_DENSITY[16])
plt.show()

These features are returned as three dimensional arrays.

fig, axs = plt.subplots(1, 3, sharex=True)
axs[0].imshow(BFF_TILE_PENALTY[16])
axs[1].imshow(PM_TILE_COST[16])
axs[2].imshow(PM_TILE_COST_DENSITY[16])
plt.show()